Papers by Qiao Liu

Grafting Pre-trained Models for Multimodal Headline Generation (2022.emnlp-industry)

Challenge:	Relation extraction (RE) is an essential topic in natural language processing and has attracted extensive attention.
Approach:	They propose a case-oriented construction framework to build a hard case relation extraction dataset with 65,225 relational facts annotated from 9,231 documents.
Outcome:	The proposed model achieves a high 96% F1 score on data quality and is far lower than humans.

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Lingfeng Qiao, Chen Wu, Ye Liu, Haoyuan Peng, Di Yin, Bo Ren

SCE: Semantic Consistency Enhanced Reinforcement Learning for Multi-Hop Knowledge Graph Reasoning (2025.findings-emnlp)

Challenge:	Existing approaches to generate video headlines with pre-trained language models are labor intensive and impractical.
Approach:	They propose to graft the encoder from the pre-trained video-language model on the generative pre-trainer model and propose a consensus fusion mechanism for the integration of different components.
Outcome:	The proposed model achieves strong results on a brand-new dataset collected from real-world applications.

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Yanwen Huang, Yao Liu, Qiao Liu, Rui Hou, Tingting Dai

GEMS: Generation-Based Event Argument Extraction via Multi-perspective Prompts and Ontology Steering (2025.findings-acl)

Challenge:	Existing approaches to multihop reasoning fail to address the problem of spurious paths . existing approaches neglect the internal semantic consistency of the reward function .
Approach:	They propose a framework that incorporates semantic consistency into the reward function to guide multi-hop reasoning.
Outcome:	The proposed framework outperforms baseline methods and facilitates more interpretable reasoning paths.

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Run Lin, Yao Liu, Yanglei Gan, Yuxiang Cai, Tian Lan, Qiao Liu

MMRC: A Large-Scale Benchmark for Understanding Multimodal Large Language Model in Real-World Conversation (2025.acl-long)

Challenge:	Existing methods for event argument extraction rely on a single prompt . existing methods ignore complex structural and dynamic interdependencies between event arguments .
Approach:	They propose a multi-prompt learning framework that generates event arguments via multi-perspective prompts and ontology steering.
Outcome:	The proposed framework captures interrelationships between arguments and ontology steering . it uses multiple unfilled prompts for each sentence to generate event arguments .

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Haochen Xue, Feilong Tang, Ming Hu, Yexin Liu, Qidong Huang, Yulong Li, Chengzhi Liu, Zhongxing Xu, Chong Zhang, Chun-Mei Feng, Yutong Xie, Imran Razzak, Zongyuan Ge, Jionglong Su, Junjun He, Yu Qiao

Natural Language Interfaces

Challenge:	Existing multimodal large language models lack the ability to memorize, recall, and reason in sustained interactions.
Approach:	They propose a multimodal real-world conversation benchmark for evaluating open-ended abilities of multimodal large language models.
Outcome:	The proposed benchmarks show that the models perform better in open-ended conversations.

P-FOLIO: Evaluating and Improving Logical Reasoning with Abundant Human-Written Reasoning Chains (2024.findings-emnlp)

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Simeng Han, Aaron Yu, Rui Shen, Zhenting Qi, Martin Riddell, Wenfei Zhou, Yujie Qiao, Yilun Zhao, Semih Yavuz, Ye Liu, Shafiq Joty, Yingbo Zhou, Caiming Xiong, Dragomir Radev, Rex Ying, Arman Cohan

GenPoE: Generative Passage-level Mixture of Experts for Knowledge Enhancement of LLMs (2025.findings-emnlp)

Challenge:	Existing methods on understanding the capabilities of LLMs in logical reasoning rely on binary entailment classification or synthetically derived rationales.
Approach:	They propose to annotate a human-annotated dataset consisting of diverse and complex reasoning chains for a set of realistic logical reasoning stories also written by humans.
Outcome:	The proposed model outperforms existing methods on understanding the capabilities of LLMs in logical reasoning by 10% or more.

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Xuebing Liu, Shanbao Qiao, Seung-Hoon Na

Information Retrieval

Challenge:	GenPoE is a passage-level mixture of experts for enhancing knowledge of large language models.
Approach:	They propose a novel “generative” passage-level mixture of experts (MoEs) that takes in-context retrieved passages and generates their “expert” parameters.
Outcome:	The proposed system is based on a novel hypernetwork which takes in-context retrieved passages and generates their "expert'' parameters.

Mixture-of-Minds: Multi-Agent Reinforcement Learning for Table Understanding (2026.acl-long)

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Yuhang Zhou, Mingrui Zhang, Ke Li, Mingyi Wang, Qiao Liu, Qifei Wang, Jiayi Liu, Fei Liu, Serena Li, Weiwei LI, Mingze Gao, Abhishek Kumar, Xiangjun Fan, Zhuokai Zhao, Lizhu Zhang

Flooding-X: Improving BERT’s Resistance to Adversarial Attacks via Loss-Restricted Fine-Tuning (2022.acl-long)

Challenge:	Large language models (LLMs) have shown promise on understanding and reasoning over tables, but current approaches remain limited.
Approach:	They propose a multi-agent framework that decomposes table reasoning into three specialized roles: planning, coding, and answering.
Outcome:	The proposed framework decomposes table reasoning into three specialized roles: planning, coding, and answering.

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Qin Liu, Rui Zheng, Bao Rong, Jingyi Liu, ZhiHua Liu, Zhanzhan Cheng, Liang Qiao, Tao Gui, Qi Zhang, Xuanjing Huang

Robustness in NLP

Challenge:	Existing approaches to generating adversarial perturbations scale up the cost of training computational complexity by the number of gradient steps it takes to obtain the adversarials.
Approach:	They propose a flood method which aims at better generalization and a criterion to bring hyper-parameter-dependent flooding into effect with a narrowed-down search space by measuring how the gradient steps taken within one epoch affect the loss of each batch.
Outcome:	The proposed method improves BERT’s resistance to textual adversarial attacks by a large margin and achieves state-of-the-art robust accuracy on various text classification and GLUE tasks.

DISC: Plug-and-Play Decoding Intervention with Similarity of Characters for Chinese Spelling Check (2025.acl-long)

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Ziheng Qiao, Houquan Zhou, Yumeng Liu, Zhenghua Li, Min Zhang, Bo Zhang, Chen Li, Ji Zhang, Fei Huang

Syntactic Text Processing

Challenge:	Chinese spelling check (CSC) tasks require that incorrect characters are usually similar to the correct ones in either phonetics or glyph.
Approach:	They propose a plug-and-play decoding intervention with similarity of characters module for Chinese spelling check (CSC) they propose to incorporate phonetic and glyph similarities only during the inference phase.
Outcome:	The proposed method significantly improves Chinese spelling check models on benchmarks and on benchmark datasets.

TCPO: Thought-Centric Preference Optimization for Effective Embodied Decision-making (2025.emnlp-main)

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Kechen Jiao, Zhirui Fang, Jiahao Liu, Bei Li, Qifan Wang, Xinyu Liu, Junhao Ruan, Zhongjian Qiao, Yifan Zhu, Yaxin Xu, Jingang Wang, Xiu Li

Inference-Time Language Model Alignment via Integrated Value Guidance (2024.findings-emnlp)

Challenge:	Existing post-SFT methods for embodied AI are constrained by sparse rewards and action-only optimization, resulting in low sample efficiency, poor consistency, and model degradation.
Approach:	They propose to integrate Thought-Centric Preference Optimization (TCPO) into embodied decision-making by transforming sparse reward signals into richer step sample pairs.
Outcome:	The proposed approach achieves an average success rate of 26.67% in the ALFWorld environment, and a 6% improvement over RL4VLM.

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Zhixuan Liu, Zhanhui Zhou, Yuanfu Wang, Chao Yang, Yu Qiao

Towards Tracing Trustworthiness Dynamics: Revisiting Pre-training Period of Large Language Models (2024.findings-acl)

Challenge:	Large language models are fine-tuned to align with human preferences, but tuning large models is computationally intensive and complex.
Approach:	They propose a method that uses implicit and explicit value functions to guide language model decoding at token and chunk-level respectively.
Outcome:	The proposed method outperforms traditional methods and circumvents the complexities of fine-tuning.

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Chen Qian, Jie Zhang, Wei Yao, Dongrui Liu, Zhenfei Yin, Yu Qiao, Yong Liu, Jing Shao

Predicting Clinical Trial Results by Implicit Evidence Integration (2020.emnlp-main)

Challenge:	Existing studies focus on pre-trained LLMs to better understand and improve their trustworthiness.
Approach:	They apply linear probing to LLMs to explore five key dimensions of trustworthiness: reliability, privacy, toxicity, fairness, and robustness.
Outcome:	The proposed model can distinguish concepts in each trustworthiness dimension, suggesting that it can be trained in early pre-training.

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Qiao Jin, Chuanqi Tan, Mosha Chen, Xiaozhong Liu, Songfang Huang

Reasoning

Challenge:	Clinical trials are expensive and time-consuming, and inappropriately designed studies can be devastating in a pandemic.
Approach:	They propose a model that takes a PICO-formatted clinical trial proposal and predicts the outcome from it.
Outcome:	The proposed model outperforms baseline models on a benchmark dataset with 10.7% relative gain over BioBERT.

Progressive Planning and Reinforced Reasoning: Large Language Model-Guided Multi-hop Question Answering over Knowledge Graph (2026.findings-acl)

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Xiang Li, Runhai Jiao, Ruifan Li, Dongnan Wu, Ruojiao Qiao, Lei Liu

Dr. Assistant: Enhancing Clinical Diagnostic Inquiry via Structured Diagnostic Reasoning Data and Reinforcement Learning (2026.findings-acl)

Challenge:	Existing approaches to multi-hop question answering lack effective intermediate guidance and policy networks focus on local neighborhood information, making it difficult to anticipate the long-term consequences of decisions.
Approach:	They propose a framework that converts decomposed sub-question sequences into stepwise decision guidance and a structure-aware lookahead policy network to enhance the agent's global state awareness and decision foresight in complex environments.
Outcome:	The proposed framework surpasses state-of-the-art methods while showing strong generalization.

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Yue Guo, Fanfu Wang, Jianwei Lv, Xincheng Shi, Yuchen Li, Youya Wang, Yunsheng Zeng, Yujing Liu, Yunhao Qiao, Gen Li, Junfeng Wang, Bo Yuan

Reasoning

Challenge:	Clinical Decision Support Systems (CDSSs) provide reasoning and inquiry guidance for physicians, yet they face high maintenance costs and low generalization capability.
Approach:	They propose a clinical diagnostic model with clinical reasoning and inquiry skills, the Dr. Assistant, and a pipeline to capture abstract reasoning logic.
Outcome:	The proposed model outperforms open-source models and achieves competitive performance to closed-source model.

PersLEARN: Research Training through the Lens of Perspective Cultivation (2023.acl-demo)

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Yu-Zhe Shi, Shiqian Li, Xinyi Niu, Qiao Xu, Jiawen Liu, Yifan Xu, Shiyu Gu, Bingru He, Xinyang Li, Xinyu Zhao, Zijian Zhao, Yidong Lyu, Zhen Li, Sijia Liu, Lin Qiu, Jinhao Ji, Lecheng Ruan, Yuxi Ma, Wenjuan Han, Yixin Zhu

Challenge:	PersLEARN is a tool designed to facilitate the cultivation of scientific perspectives . junior researchers struggle to identify the perspectives reflected in the literature and struggle to develop their own viewpoints.
Approach:	They propose a tool to facilitate the cultivation of scientific perspectives by interacting with a prompt-based model and allowing students to develop their own perspectives explicitly.
Outcome:	The proposed tool outperforms baseline approaches across multiple domains of literature from different perspectives.

Beyond One-Preference-Fits-All Alignment: Multi-Objective Direct Preference Optimization (2024.findings-acl)

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Zhanhui Zhou, Jie Liu, Jing Shao, Xiangyu Yue, Chao Yang, Wanli Ouyang, Yu Qiao

COMEM: In-Context Retrieval-Augmented Mass-Editing Memory in Large Language Models (2024.findings-naacl)

Challenge:	Recent approaches to language model alignment assume homogeneous human preferences, but actual human preferences vary widely and are hard to satisfy with a single language model.
Approach:	They propose an RL-free extension of Direct Preference Optimization (DPO) that folds language modeling directly into reward modeling and trains language models as collective reward models that combine all objectives with specific weights.
Outcome:	The proposed method matches or outperforms existing methods in safety alignment and long-form question answering.

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Shanbao Qiao, Xuebing Liu, Seung-Hoon Na

MathBench: Evaluating the Theory and Application Proficiency of LLMs with a Hierarchical Mathematics Benchmark (2024.findings-acl)

Challenge:	Existing methods for "knowledge editing" in large language models are inadequate . authors propose a method that can be used to update outdated information or correct false information .
Approach:	They propose a unified knowledge editing method called in-COntext retrieval-augmented Mass-Editing Memory . it incorporates retrieval augmented IKE, a novel extension of IKE designed for massive editing tasks .
Outcome:	The proposed method outperforms existing methods on the zsRE and CounterFact datasets.

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Hongwei Liu, Zilong Zheng, Yuxuan Qiao, Haodong Duan, Zhiwei Fei, Fengzhe Zhou, Wenwei Zhang, Songyang Zhang, Dahua Lin, Kai Chen

Numerical Reasoning

Challenge:	Recent advances in large language models have showcased significant improvements in mathematics, but traditional benchmarks like GSM8k offer a unidimensional perspective.
Approach:	MathBench is a benchmark that rigorously assesses the mathematical capabilities of large language models.
Outcome:	MathBench spans a wide range of mathematical disciplines, offering a detailed evaluation of both theoretical understanding and practical problem-solving skills.

OS-Symphony: A Holistic Framework for Robust and Generalist Computer-Using Agents (2026.acl-long)

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Bowen Yang, Kaiming Jin, Zhenyu Wu, Zhaoyang Liu, Qiushi Sun, Zehao Li, JingJing Xie, Zhoumianze Liu, Fangzhi Xu, Kanzhi Cheng, Yian Wang, Qingyun Li, Yu Qiao, Zun Wang, Zichen Ding

Robustness in NLP

Challenge:	Vision-Language Models (VLMs) lack visual-aware tutorial retrieval and historical visual context curation and pruning.
Approach:	They propose a framework that integrates an orchestrator and a Reflection-Memory Agent for robust automation.
Outcome:	Experimental results show that OS-Symphony delivers substantial performance gains across model scales.

Negative-Aware Diffusion Process for Temporal Knowledge Graph Extrapolation (2026.findings-eacl)

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Yanglei Gan, Peng He, Yuxiang Cai, Run Lin, Guanyu Zhou, Qiao Liu

OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis (2025.acl-long)

Challenge:	Temporal Knowledge Graphs (TKGs) are dynamic structures representing entities and their evolving relationships through time.
Approach:	They propose a non-parametric model that encodes subject-centric histories into sequential embeddings.
Outcome:	The proposed model encodes subject-centric histories of entities, relations and temporal intervals into sequential embeddings.

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Qiushi Sun, Kanzhi Cheng, Zichen Ding, Chuanyang Jin, Yian Wang, Fangzhi Xu, Zhenyu Wu, Chengyou Jia, Liheng Chen, Zhoumianze Liu, Ben Kao, Guohao Li, Junxian He, Yu Qiao, Zhiyong Wu

Why Do Emotions Change? Appraisal-Guided Reasoning for Emotion–Cause Triplet Extraction in Conversations (2026.acl-long)

Challenge:	Graphical User Interface (GUI) agents powered by Vision-Language Models (VLMs) have demonstrated human-like computer control capability.
Approach:	They propose a GUI data synthesis pipeline that reverse engineers GUI trajectory construction process by executing pre-defined tasks.
Outcome:	The proposed GUI data synthesis pipeline overcomes the bottlenecks of previous methods that rely on pre-defined tasks and limited data diversity.

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Qiao Liang, Ying Shen, Yao Liu, Tiantian Chen, Lin Zhang

Uni-MMMU: A Massive Multi-discipline Multimodal Unified Benchmark (2026.acl-long)

Challenge:	Existing methods for multi-turn, multi-speaker multimodal affect understanding are difficult to maintain conversation-level consistency under within-speaks' emotion shifts.
Approach:	They propose a framework that combines appraisal-guided structured generation with graph-structured reinforcement learning to extract triplets from multi-turn multimodal conversations.
Outcome:	The proposed framework outperforms baselines on public MECTEC benchmarks and improves structure-aware metrics on emotion shift coherence and core events.

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Kai Zou, Ziqi Huang, Yuhao Dong, Shulin Tian, Dian Zheng, Hongbo Liu, Jingwen He, Bin Liu, Yu Qiao, Ziwei Liu

Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models (2024.acl-long)

Challenge:	Existing evaluations treat visual understanding and generation in isolation or overlook tasks that inherently couple them.
Approach:	They propose a benchmark that examines the bidirectional synergy between generation and understanding across eight reasoning-centric domains.
Outcome:	The proposed model systematically unfolds the bidirectional synergy between generation and understanding across eight reasoning-centric domains.

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Fangzhi Xu, Zhiyong Wu, Qiushi Sun, Siyu Ren, Fei Yuan, Shuai Yuan, Qika Lin, Yu Qiao, Jun Liu

LLMs know their vulnerabilities: Uncover Safety Gaps through Natural Distribution Shifts (2025.acl-long)

Challenge:	Large Language Models (LLMs) have limitations when it comes to comprehending and expressing world knowledge that extends beyond the boundaries of natural language.
Approach:	They propose a model that integrates symbolic data into LLM training without loss of generality ability.
Outcome:	The proposed model performs better on symbol- and NL-centric tasks.

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Qibing Ren, Hao Li, Dongrui Liu, Zhanxu Xie, Xiaoya Lu, Yu Qiao, Lei Sha, Junchi Yan, Lizhuang Ma, Jing Shao

Responsible & Trustworthy NLP

Challenge:	Current safety training focuses on teaching models to reject harmful queries, but recent research shows that adversarial attacks or jailbreak methods bypass these safety mechanisms.
Approach:	They propose to use a new attack method to craft multi-turn toxic prompts that gradually lead LLMs to reveal unsafe content.
Outcome:	The proposed method outperforms existing methods in diversity, effectiveness, and efficiency across aligned LLMs.

DistillMIKE: Editing Distillation of Massive In-Context Knowledge Editing in Large Language Models (2024.findings-acl)

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Shanbao Qiao, Xuebing Liu, Seung-Hoon Na

DiFiNet: Boundary-Aware Semantic Differentiation and Filtration Network for Nested Named Entity Recognition (2024.acl-long)

Challenge:	In-context knowledge editing has shown respectable abilities on knowledge editing in terms of generalization and specificity.
Approach:	They propose a novel extension of in-context knowledge editing (IKE) that allows for massive edits to be injected into large language models.
Outcome:	The proposed method shows state-of-the-art perfomrances and comparable performance with MIKE.

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Yuxiang Cai, Qiao Liu, Yanglei Gan, Run Lin, Changlin Li, Xueyi Liu, Da Luo, JiayeYang JiayeYang

Aspect and Sentiment Aware Abstractive Review Summarization (C18-1)

Challenge:	Existing approaches to Named Entity Recognition focus on identifying non-nested entities, but there is no explicit guidance for boundary detection.
Approach:	They propose a Boundary-aware Semantic Differentiation and Filtration Network for nested NER that leverages a biaffine attention mechanism to generate a span representation matrix.
Outcome:	Extensive experiments on three benchmark datasets demonstrate the proposed model yields a new state-of-the-art performance.

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Min Yang, Qiang Qu, Ying Shen, Qiao Liu, Wei Zhao, Jia Zhu

Summarization

Challenge:	Abstractive summarization is a task that generates short and concise summaries of user generated reviews.
Approach:	They propose an interactive attention mechanism to learn the representations of context and aspect words within reviews, acted as an encoder.
Outcome:	The proposed model achieves impressive results compared to other strong competitors on a real-life dataset.

AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction (2026.acl-long)

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Hong Ting Tsang, Jiaxin Bai, Haoyu Huang, Qiao Xiao, Tianshi Zheng, Baixuan Xu, Shujie Liu, Yangqiu Song

Structured Data in NLP

Challenge:	Currently, knowledge graphs are decoupled from their downstream application, resulting in suboptimal graph structures.
Approach:	They propose a framework to directly optimize KG construction for task performance using Reinforcement Learning (RL).
Outcome:	The proposed framework improves performance across multiple QA benchmarks and consistently achieves significant performance gains over task-agnostic baseline graphs.

Beyond Benchmarks: A Capability-Based Maturity Model for Systematic AI Integration in Hospitals (2026.findings-acl)

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Rui Li, Xiaofen WU, Mingqian Liu, Xiaoxia Song, Xu Xiangjun, Jiacheng Qiao, Zhuang Boqin, Xu Chen

Challenge:	Current Large Language Models (LLMs) excel in standardized tests focused on medical knowledge recall, but not in real-world healthcare scenarios.
Approach:	They propose a "capability-based hospital AI Maturity Model" framework that categorizes capabilities into distinct maturity levels . medical artificial intelligence is currently at a critical transition stage from technical verification to deep clinical integration .
Outcome:	The proposed model provides a clear, stepwise evolutionary path for hospitals from foundational infrastructure construction to ubiquitous intelligence.

Evaluation Agent: Efficient and Promptable Evaluation Framework for Visual Generative Models (2025.acl-long)

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Fan Zhang, Shulin Tian, Ziqi Huang, Yu Qiao, Ziwei Liu

Responsible & Trustworthy NLP

Challenge:	Existing evaluation methods rely on rigid pipelines that overlook user needs and provide numerical results without clear explanations.
Approach:	They propose an evaluation framework that employs human-like strategies for efficient, dynamic, multi-round evaluations using only a few samples per round.
Outcome:	The evaluation agent framework reduces evaluation time to 10% of traditional methods while delivering comparable results.

Expanding the Boundaries of Vision Prior Knowledge in Multi-modal Large Language Models (2026.eacl-long)

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Qiao Liang, Yanjiang Liu, Weixiang Zhou, Ben He, Yaojie Lu, Hongyu Lin, Jia Zheng, Xianpei Han, Le Sun, Yingfei Sun

Interactive Evolution: A Neural-Symbolic Self-Training Framework For Large Language Models (2025.acl-long)

Challenge:	Existing research treats MLLMs as unified systems optimized through end-to-end training, but the impact of vision encoder’s prior knowledge is seldom investigated.
Approach:	They propose a metric to quantify the effect of prior knowledge on MLLM performance by integrating prior knowledge at the vision encoder level into a training framework.
Outcome:	The proposed training framework incorporates prior knowledge at the vision encoder level, and significantly boosts visual understanding capabilities of MLLMs.

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Fangzhi Xu, Qiushi Sun, Kanzhi Cheng, Jun Liu, Yu Qiao, Zhiyong Wu

XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation (2020.emnlp-main)

Challenge:	Existing methods to fine-tune Large Language Models without human annotations are lacking in the field of natural language training.
Approach:	They propose an environment-guided neural-symbolic self-training framework to overcome two main challenges: the scarcity of symbolic data and the limited proficiency of LLMs in processing symbolic language.
Outcome:	The proposed framework overcomes two main challenges: the scarcity of symbolic data, and the limited proficiency of LLMs in processing symbolic language.

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Yaobo Liang, Nan Duan, Yeyun Gong, Ning Wu, Fenfei Guo, Weizhen Qi, Ming Gong, Linjun Shou, Daxin Jiang, Guihong Cao, Xiaodong Fan, Ruofei Zhang, Rahul Agrawal, Edward Cui, Sining Wei, Taroon Bharti, Ying Qiao, Jiun-Hung Chen, Winnie Wu, Shuguang Liu, Fan Yang, Daniel Campos, Rangan Majumder, Ming Zhou

Multilinguality

Challenge:	XGLUE provides a benchmark dataset to train large-scale cross-lingual pre-trained models . XCLUE provides 11 diversified tasks that cover both understanding and generation scenarios .
Approach:	They introduce a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora.
Outcome:	The proposed dataset is labeled in English and includes only natural language understanding tasks.

Emotion Trajectory-aware Retrieval for Markov-driven Emotion Anticipation in LLM-based Emotional Support Conversation (2026.findings-acl)

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Hongyan Wu, Zhiliang Tian, Zhen Huang, Tengyue Hu, Linbo Qiao, Yifu Gao, Feng Liu, Dongsheng Li

Information Retrieval

Challenge:	Existing strategies focus on planning the next-turn dialogue strategies, while external strategy planners focus on generating empathetic responses.
Approach:	They propose a Markov-driven emotion anticipation framework with emotion trajectory-aware retrieval for LLM-based ESC, which anticipates future emotion states to guide strategy planning and achieve sustained emotional support.
Outcome:	The proposed framework can anticipate future emotions and achieve sustained emotional support on two datasets with two models.

On-the-Fly VLA Adaptation via Test-Time Reinforcement Learning (2026.acl-long)

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Changyu Liu, Yiyang Liu, Taowen Wang, Qiao Zhuang, James Chenhao Liang, Wenhao Yang, Renjing Xu, Qifan Wang, Dongfang Liu, Cheng Han

MoPS: Modular Story Premise Synthesis for Open-Ended Automatic Story Generation (2024.acl-long)

Challenge:	Existing vision-language-action models are unsuitable for simulated or physical-world deployments . current methods fail when confronted with inherent real-world dynamic variability.
Approach:	They propose a test-time reinforcement learning framework that enables on-the-fly policy adaptation during inference.
Outcome:	Empirical results show that the proposed framework improves adaptability, stability and task success in dynamic, previously unseen scenarios.

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Yan Ma, Yu Qiao, Pengfei Liu

Text Generation

Challenge:	Existing sources of story premises are limited by a lack of diversity, uneven quality, and high costs that make them difficult to scale.
Approach:	They propose a method which breaks down story premises into modules like background and persona for automated design and generation.
Outcome:	The proposed framework excels in diversity, fascination, completeness, and originality compared to those induced from large language models and captured from public datasets.

GRASS: Gradient-based Adaptive Layer-wise Importance Sampling for Memory-efficient Large Language Model Fine-tuning (2026.findings-acl)

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Kaiyuan Tian, Linbo Qiao, Yu Tang, Gongqingjian Jiang, Baihui Liu, Yifu Gao, Xialin Su, Dongsheng Li

Predicting the Unpredictable: Uncertainty-Aware Reasoning over Temporal Knowledge Graphs via Diffusion Process (2024.findings-acl)

Challenge:	Low-rank adaptation methods for large language models limit expressiveness and performance . layer-wise fine-tuning methods overlook variations in layer importance across tasks and training stages, resulting in suboptimal performance on downstream tasks.
Approach:	They propose a gradient-based adaptive layer-wise importance sampling framework that updates only a subset of parameters to reduce memory usage.
Outcome:	The proposed framework outperforms state-of-the-art methods in accuracy and memory usage.

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Yuxiang Cai, Qiao Liu, Yanglei Gan, Changlin Li, Xueyi Liu, Run Lin, Da Luo, JiayeYang JiayeYang

A Framework for Effective Invocation Methods of Various LLM Services (2025.coling-main)

Challenge:	Existing methods for Temporal Knowledge Graph reasoning capture indeterminacy in future events, but they are limited in capturing it.
Approach:	They propose a Temporal Knowledge Graph reasoning process that denoises historical events and introduces Gaussian noise to corrupt target facts.
Outcome:	Empirical results show that DiffuTKG outperforms state-of-the-art methods on four real-world datasets.

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Can Wang, Dianbo Sui, Bolin Zhang, Xiaoyu Liu, Jiabao Kang, Zhidong Qiao, Zhiying Tu

Alloc-MoE: Budget-Aware Expert Activation Allocation for Efficient Mixture-of-Experts Inference (2026.acl-long)

Challenge:	Large Language Models (LLMs) are becoming a fundamental tool for various natural language processing tasks due to commercial reasons, the potential risk of misuse and expensive tuning cost.
Approach:	They propose a framework for constructing an effective LLM services invocation strategy that best meets task demands.
Outcome:	The proposed framework classifies existing methods into four categories: input abstraction, semantic cache, solution design, and output enhancement, which can be used separately or jointly during the invocation life cycle.

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Baihui Liu, Kaiyuan Tian, Wei Wang, Zhaoning Zhang, Linbo Qiao, Dongsheng Li

Green & Sustainable NLP

Challenge:	Existing approaches that reduce expert activations lead to severe model performance degradation.
Approach:	They propose a framework that optimizes budget allocation coordinately at layer and token levels to minimize model performance degradation.
Outcome:	The proposed framework achieves 1.15 prefill and 1.34 decode speedups on DeepSeek-V2-Lite at half of the original budget.

GUI0: Self-Evolving Foundational GUI Agents in Super App Ecosystems (2026.acl-long)

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Xinyi Wang, Wei Dai, Kyle Qiao, Ke Wang, Peng Chen, Gang Cao, null Kangqin, Zhongpu Wang, Xiaode Zhang, Yanming Liu, Jihao Gu, Jingtao Xu, Gong Zhi

Responsible & Trustworthy NLP

Challenge:	Automated interaction with graphical user interfaces (GUIs) is central to general artificial intelligence, but remains challenging within Super App ecosystems.
Approach:	They propose a framework synergizing autonomous data synthesis with dual-agent co-evolution . GUI0 establishes a domain-aware foundation model via synthesized corpora and employs curriculum-driven reinforcement learning .
Outcome:	The proposed framework outperforms Gemini-2.5-Pro and Claude-4-Sonnet in the SuperAPP benchmark and has universal efficacy across base models.

MIND: A Large-scale Dataset for News Recommendation (2020.acl-main)

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Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan Wu, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xie, Jianfeng Gao, Winnie Wu, Ming Zhou

Synergetic Interaction Network with Cross-task Attention for Joint Relational Triple Extraction (2024.lrec-main)

Challenge:	Personalized news recommendation is an important technique for personalized news service.
Approach:	They propose to build a large-scale news recommendation dataset from Microsoft News . they demonstrate that news recommendation relies on the quality of news content understanding .
Outcome:	The proposed dataset contains 1 million users and more than 160k English news articles, each of which has rich textual content such as title, abstract and body.

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Da Luo, Run Lin, Qiao Liu, Yuxiang Cai, Xueyi Liu, Yanglei Gan, Rui Hou

MADAWSD: Multi-Agent Debate Framework for Adversarial Word Sense Disambiguation (2025.emnlp-main)

Challenge:	Existing approaches to joint entity-relation extraction are limited in their ability to capture the interdependence between the two sub-tasks.
Approach:	They propose a synergistic approach to capture interdependence between named entity recognition and relation extraction sub-tasks in a Synergetic Interaction Network.
Outcome:	The proposed model achieves significantly better performance on three benchmark datasets.

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Kaiyuan Zhang, Qian Liu, Luyang Zhang, Chaoqun Zheng, Shuaimin Li, Bing Xu, Muyun Yang, Xinxiao Qiao, Wenpeng Lu

Robustness in NLP

Challenge:	Word sense disambiguation (WSD) is a fundamental yet challenging task in natural language processing.
Approach:	a novel multi-agent Debate framework for adversarial word Sense disambiguation is proposed . the framework simulates a real-world debate environment where multiple agents engage in discussions about ambiguous words in the context of adversarials.
Outcome:	The proposed framework integrates with existing LLMs and improves models in Chinese language . it shows that it can be used to improve models in the Chinese language and improve performance .

DongbaMIE: A Multimodal Information Extraction Dataset for Evaluating Semantic Understanding of Dongba Pictograms (2025.findings-emnlp)

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Xiaojun Bi, Shuo Li, Junyao Xing, Ziyue Wang, Fuwen Luo, Weizheng Qiao, Lu Han, Ziwei Sun, Peng Li, Yang Liu

DiffusionRet: Diffusion-Enhanced Generative Retriever using Constrained Decoding (2023.findings-emnlp)

Challenge:	Dongba pictographic is the only pictograph script still in use in the world.
Approach:	DongbaMIE is the first dataset focusing on multimodal information extraction of Dongbe pictographs.
Outcome:	The dataset contains 23,530 sentence-level and 2,539 paragraph-level high-quality text-image pairs.

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Shanbao Qiao, Xuebing Liu, Seung-Hoon Na

Information Retrieval

Challenge:	Generative retrieval methods have suffered from the lack of the intermediate reasoning step . generative retrieval uses sequence-to-sequence diffusion models to map a query to relevant docids .
Approach:	They propose a novel method that uses query as an intermediate step before retrieval . they propose to use sequence-to-sequence diffusion models to map a query to relevant docids .
Outcome:	Experiments show that proposed method outperforms existing methods on MARCO and Natural Questions datasets.

PubMedQA: A Dataset for Biomedical Research Question Answering (D19-1)

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Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William Cohen, Xinghua Lu

Question Answering

Challenge:	PubMedQA is a biomedical question answering dataset based on PubMed abstracts . 68.1% accuracy is achieved, compared to single human performance of 78.0% .
Approach:	They propose a biomedical question answering dataset from PubMed abstracts . the dataset is annotated by experts and has 1k instances of QA .
Outcome:	The proposed model achieves 68.1% accuracy compared to human performance of 78.0% and majority-baseline of 55.2%.

SeqMMR: Sequential Model Merging and LLM Routing for Enhanced Batched Sequential Knowledge Editing (2025.findings-acl)

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Shanbao Qiao, Xuebing Liu, Akshat Gupta, Seung-Hoon Na